Global organic carbon emissions from primary sources from 1960 to 2009

Atmospheric Environment 122 (2015) 505e512

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Global organic carbon emissions from primary sources from 1960 to 2009 Ye Huang, Huizhong Shen, Yilin Chen, Qirui Zhong, Han Chen, Rong Wang, Guofeng Shen, Junfeng Liu, Bengang Li, Shu Tao* Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

 An OC emission inventory with 70 detailed source information was compiled.  Newly published emission factors, especially those in developing countries, were taken into consideration.  A new fuel-consumption data product (PKU-Fuel-2007) was used to reduce spatial bias of this inventory.  Global OC emissions peaked around the year 1990 and future emission reduction is expected.  Emission intensity and the OC/PM2.5 ratio for the primary emissions have decreased continuously.

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Article history: Received 5 February 2015 Received in revised form 17 September 2015 Accepted 7 October 2015 Available online xxx

In an attempt to reduce uncertainty, global organic carbon (OC) emissions from a total of 70 sources were compiled at 0.1  0.1 resolution for 2007 (PKU-OC-2007) and country scale from 1960 to 2009. The compilation took advantage of a new fuel-consumption data product (PKU-Fuel-2007) and a series of newly published emission factors (EFOC) in developing countries. The estimated OC emissions were 32.9 Tg (24.1e50.6 Tg as interquartile range), of which less than one third was anthropogenic in origin. Uncertainty resulted primarily from variations in EFOC. Asia, Africa, and South America had high emissions mainly because of residential biomass fuel burning or wildfires. Per-person OC emission in rural areas was three times that of urban areas because of the relatively high EFOC of residential solid fuels. Temporal trend of anthropogenic OC emissions depended on rural population, and was influenced primarily by residential crop residue and agricultural waste burning. Both the OC/PM2.5 ratio and emission intensity, defined as quantity of OC emissions per unit of fuel consumption for all sources, of anthropogenic OC followed a decreasing trend, indicating continuous improvement in combustion efficiency and control measures. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Organic carbon Global emission Temporal trend Emission intensity OC/PM2.5 ratio

1. Introduction

* Corresponding author. E-mail address: [email protected] (S. Tao). http://dx.doi.org/10.1016/j.atmosenv.2015.10.017 1352-2310/© 2015 Elsevier Ltd. All rights reserved.

Organic carbon (OC) is a major component of aerosol in the atmosphere, and is formed primarily by incomplete combustion or the oxidation of gas-phase precursors (Cooke et al., 1999;

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Kanakidou et al., 2005). Unlike black carbon, OC cool the atmosphere by scattering radiation (Kanakidou et al., 2005). Liousse et al. (1996) made the first attempt to estimate global OC emissions covering both biomass and fossil fuels. Cooke et al. (1999) developed inventories for developed, semi-developed, and developing countries separately, by considering the differences in combustion efficiencies and emission factors (EFOC). The method was modified to estimate global emissions of carbonaceous aerosols, including OC, from 1860 to 1997 (Junker and Liousse, 2008). A technology-split method based on the fraction of each technology in use was proposed, allowing country compilations of EF values for specific sources (Klimont et al., 2002). Emission inventories still have large uncertainties in such factors as activity rate, EFOC, and technology division (Bond et al., 2004; Wang et al., 2012). Of these, the variation in EFOC contributes the most because of dissimilarities in fuel properties, combustion devices, operations, and even measurement methods (Cooke et al., 1999; Junker and Liousse, 2008). Reported EFOC for a given source can vary by up to three orders of magnitude (Junker and Liousse, 2008). Such uncertainty is magnified by the fact that only a few EFOC were reported in developing countries (Junker and Liousse, 2008). This study was undertaken to update the OC emission inventory for 2007, taking advantage of a recently published fuelconsumption data product (PKU-FUEL-2007) (Wang et al., 2013). The most important goal was to achieve an inventory of high sectorial (70 detailed sources) and spatial (0.1  0.1 ) resolution (PKU-OC-2007; http://inventory.pku.edu.cn/). A series of newly reported EFOC for residential fuel consumption in developing countries was used. This was important because the residential sector in developing countries is the dominant OC emission source (Shen et al., 2010). Emissions at the country scale were also estimated from 1960 to 2009, and updated information on technology divisions in China were included (Zhao et al., 2011). Based on the results, spatiotemporal variations of OC emission density and intensity, and the relationship between OC emissions and PM2.5 emissions are discussed. 2. Methodology 2.1. Emission estimation Emission from a specific source was derived as a product of quantity of fuel consumed or material produced by EFOC. In addition to 65 combustion sources (Wang et al., 2013), five process sources included are sintering, pig iron production, open-hearth furnace, convertor, and arc furnace in iron and steel industry (World Steel Assoication, 2012). Detailed source information is listed in Table S1. For years other than 2007, annual country fuelconsumption data, with slight modifications, are from Shen et al. (2013b) (Table S1). 2.2. Emission factors An EFOC database was compiled, based on a literature review. For power generation, industrial, and residential sectors, only directly measured data were collected (Table S1). Many EFOC for developing countries which were not used in previous inventories was included (Bond et al., 2004; Junker and Liousse, 2008). For several activities, EFOC were converted from EFPM using constant OC/PM ratios listed in Table S1. 2.3. Technology divisions For coal usage in the energy and industrial sectors, EFOC depends

on the combination of boilers (pulverized coal or stoker) and abatement equipment (uncontrolled, cyclones, wet scrubber, electrostatic precipitator, or fabric filter). These technologies are country and time dependent (Huang et al., 2014). Bituminous coal consumption in the residential sector of China was subdivided into chunk coal and honeycomb briquettes, each with a different EF value (Table S1). The fraction of biomass fuels consumed in stoves and fireplaces, and the percentage of improved woodstoves, were from the literature (Bond et al., 2004; Shen et al., 2013a). 2.4. Uncertainty analysis A Monte Carlo simulation was run 10,000 times to characterize the overall uncertainty. It was assumed that fuel consumption was uniformly distributed, with coefficients of variation of 10%, 20%, 30%, 20%, and 30% for energy generation, industry, residential, transportation, and open biomass burning, respectively (Shen et al., 2013b). The statistics of the log-normally distributed EFOC were taken directly from the database (Table S1) (Shen et al., 2013b; Wang et al., 2014). The coefficients of variation of penetration rates of abatement facilities in the power and industry sectors, and the ratios of woodstove and fireplace were all assumed to be 50%. Monte Carlo simulations were also conducted for individual variables to characterize their contributions to total uncertainty. 3. Results and discussion 3.1. Global emissions in 2007 In 2007, the total emissions of OC from primary sources was 32.9 Tg (24.1 Tg and 50.6 Tg as 25th and 75th percentiles), of which 32.8% was from anthropogenic sources. Excluding agricultural waste burning, the anthropogenic emissions in 2000 and 2007 were 9.22 Tg and 9.30 Tg. These results are different from those reported previously (8.7 Tg for 2000 (Bond et al., 2007) and 7.26 Tg for 1997 (Junker and Liousse, 2008); 11.99 Tg and 11.95 Tg for 2000 and 2007 in MACCity) (Lamarque et al., 2010), primarily due to the differences in EFOC applied. Because of the difference in biomass fuel consumption in the Indian residential sector, the estimated data of anthropogenic OC emissions in Asia in 2000 (7.07 Tg) is much lower than the previously reported value of 8.88 Tg (Ohara et al., 2007). In addition, based on newly reported lower EFOC for industrial sources, the estimated total emissions in China in 2000 (3.60 Tg) is lower than 4.24 Tg reported previously (Cao et al., 2006). Fig. S1 displays the relative contribution of each sector. Similar to BC and polycyclic aromatic hydrocarbons (PAHs) (Wang et al., 2014; Shen et al., 2013b), the residential sector contributed 72.2% of anthropogenic emissions, while accounting for only 11.7% of the total anthropogenic energy consumption (Wang et al., 2013). The high emissions are due to low combustion efficiency and the absence of abatement facilities. If agricultural wastes and wildfires are included, 91.6% of total emissions would be from biomass burning. Significant differences for some individual sources can be found. For example, the contributions of the industrial and transportation sectors in the former are 1.9 and 4 times those of the latter. At a global scale, the average per-person OC emissions were 1.73 kg/year. Although per person fuel consumption in developed countries was much higher than that of developing countries (Wang et al., 2013), per-person OC emission of developed countries (0.93 kg/year) was approximately half that of developing countries (1.91 kg/year). This pattern reflects differences in both source patterns and EFOC. Residents in developing countries are heavily dependent on coal and biomass fuel, while electricity and other

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clean fuels are predominant in the residential sector of developed countries (Wang et al., 2013). In China alone, fuel wood and crop residues are used in more than 130 million rural households (CHNS, 2014). Combustion efficiencies of coal and biomass fuels used in the residential sector are much lower than those burned in industrial facilities (Gonçalves et al., 2011). Products manufactured in developing countries are also very different from those in developed countries. For example, 77.8% of coke (IEA, 2012) and 86.6% of bricks (UNIDO, 2008) were produced in developing countries using less advanced facilities (Bond et al., 2007; Zhao et al., 2011), thereby generating large quantities of OC. EFOC for energy generation, industrial activities, and transportation in developed countries are much lower than those in developing countries because of advanced facilities and emissioncontrol technologies. For example, though almost all power plants have some degree of control, the ratios of high efficient remove facilities, such as fabric filter used in power stations in developed countries such as Australia (>80%) (Yao et al., 2005) and the United States (>23%) (USEIA, 2014), are much higher than those in developing countries (<5%) (Zhao et al., 2011). In addition, the Euro IV for both gasoline and diesel engines was enforced in 2005 in Europe (Timilsina and Dulal, 2009) while an equivalent standard, the China IV, was not introduced in China until 2008 (CNR, 2013). 3.2. Geographical distribution Geographical distributions of total annual (including wildfires and deforestation) and per-person (anthropogenic) emissions are presented in Fig. 1, showing spatial variation patterns similar to those of BC and PAHs (Wang et al., 2014; Shen et al., 2013b). Although Asia (26.9%), Africa (38.4%), and South America (19.9%) are all hot regions in terms of total emissions, the source patterns are very different. In the Congo River Basin and Amazon Basin, 85.6% of OC was from wildfires and deforestation (van der Werf et al., 2010), while high emissions in Asia are largely a result of the combined effect of heavy use of solid fuels and high population density. Residential emissions of OC in China and India reached 2.28 Tg and 1.22 Tg, respectively. The spatial variation of per-person anthropogenic OC emissions was very different from that of total emissions. The relatively high values found in the Northern Cape of South Africa and Mianyang, Taiyuan, and Erdos of China were because of rich coal reserves and extensive use of coal in the residential sector (SSA, 2009; NBSC, 2011). Relatively high emissions in Nigeria, Gabon, Montana and North Dakota in the United States, and in Mizoram and Manipur, India, were due to heavy use of biomass (IEA, 2012; FAO, 2012; CEIP, 2012). OC emissions from specific sectors in various regions were compared with those reported previously in MACCity (Lamarque

et al., 2010) in Fig. 2. For total anthropogenic emissions, although the difference in the global total is only 8.7% between MACCity (11.73 Tg, excluding aviation and navigation) and the study result (10.75 Tg), differences for individual regions are much larger, with a mean absolute difference of 30.2%. The largest difference can be found in South America (54.6%), likely due to the adoption of a relatively lower EFOC for the industrial, power plants, transportation, and residential sector in this study. Significant differences can also be found among numerous anthropogenic sources. For example, country and time specific EFOC were applied in this study (Huang et al., 2014), leading to much lower motor vehicle emission than those in MACCity (Bond et al., 2007; Junker and Liousse, 2008; Lamarque et al., 2010). Our estimates of agricultural waste burning in East Asia, South and Southeast Asia, and South America were higher than those in MACCity because of a difference in the amount of crop residue burned in the field (Bond et al., 2007; Wang et al., 2013). Based on relatively high EFOC for coal and low EFOC for wood (Bond et al., 2007; Junker and Liousse, 2008), emissions were high for East Asia and low for other regions. The 0.1  0.1 resolution emission inventory provides an opportunity to compare emissions between urban and rural areas. The method used by Wang et al. (2013) was adopted to classify all grids into urban or rural areas. Despite the average per-person energy consumption in urban areas being three times higher than that in rural areas (Wang et al., 2013), average per-person annual emission in urban areas was only 0.71 kg, which was less than one third of that in rural areas (2.5 kg). This result is primarily caused by the fact that rural residents relied more on solid fuels, and the residential sector dominated total emissions. Although industrial and transportation sources are typically located in urban areas, their EFOC are several orders of magnitude lower than those of residential sources. An important implication of such large differences is that total OC emissions in transition countries will be strongly affected by rapid urbanization in the future. The rural-tourban migrants face a rapid alteration in their energy use as they ascend the energy ladder (Masera et al., 2000). In fact, the increasing trend of total anthropogenic OC emissions in developing countries stopped two decades ago (next section) and is expected to decline further in the future. Another immediate result of rapid and large-scale urbanization is that sources and receptors are aggregated to cities, leading to even higher population respiration exposure. In 2007, global mean annual OC emission densities reached 466 kg/km2 in urban and 64 kg/km2 in rural areas. This gap is expected to increase because of urbanization. 3.3. Temporal trend Historically anthropogenic and natural emissions from 1960 to

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2009 are shown in Fig. 3. The mean emissions and differences between the 25th and 75th percentiles are presented. The emissions reported in the literature, either as time series (lines) or for individual years (dots), are displayed for comparison. The study results are generally higher than those reported except by MACCity. The differences are mainly caused by higher EFOC for residential coal in the new database (Bond et al., 2007; Junker and Liousse, 2008). The relatively high values of MACCity are likely because of higher EFOC applied in power, industrial, and transportation sectors (Lamarque et al., 2010). With one exception (Junker and Liousse, 2008), the decrease after 1990 was not shown in other studies. The study inventory also indicates inter-annual variation from wildfires and deforestation sources, using annual data (Schultz et al., 2008; van der Werf et al., 2010). Global anthropogenic emissions of OC increased continuously from 1960 to 1990, during which rural populations in developing countries increased from 1.7  109 to 2.9  109 (WB, 2012), and heavy dependence on solid fuels for cooking and heating persisted (IEA, 2012). The rural population in developing countries increased only 13.4% between 1990 and 2009, and actually decreased in China because of urbanization (WB, 2012). Replacement of solid fuels and promotion of centralized heating systems (IEA, 2012; NBS&NEA, 2009) reduced the per-person annual OC emissions in developing countries from 2.46 kg in 1990 to 1.76 kg in 2009. In China, the proportion of liquefied petroleum gas (LPG), natural gas, and biogas consumed in the residential sector increased from 1.3% in 1990, to 14.0% in 2009 (NBS&NEA, 2009). A sharp decrease in industrial coal

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consumption due to the worldwide recession in the 1990s (IEA, 2012), resulted in lower industrial emissions. Fig. 4 presents the relative changes of anthropogenic OC emissions for five economic sectors and six fuel categories since 1960. Emissions from transportation and power stations decreased from the early 1970s in most developed countries. In developed countries, OC emissions from transportation and power generation began to decline in the early 1970s, soon after enforcement of early environmental regulations, such as the U.S. Clean Air Act (USEPA, 2010). Similar changes in developing countries occurred two decades later. In China, emissions from power stations and motor vehicles were not, as a practical matter, regulated until 1983 and 1991, when standards GB 3842-83 and GB13223-91 were enforced (GB3842-83, GB13223-91). Industrial emissions continued to expand, driven mainly by rapid increases in coal use and the production of coke and bricks (IEA, 2012). In China alone, industrial coal consumption increased from 60 MT in 1960 (UNSD, 1995) to 2118 MT in 2009 (IEA, 2012). Fig. 5 shows temporal trends of anthropogenic emissions from various sectors (top) and residential consumption of various fuels for East Asia (A), South and Southeast Asia (B), Europe (C), and North America (D). Although anthropogenic OC emissions increased slowly worldwide, rapid changes can be seen in individual regions. The total emissions in East Asia more than doubled from 1960 (1900 Gg) to 1990 (3921 Gg), mainly as a result of the increase in coal and biomass fuel consumption in the residential sector (IEA, 2012). The emissions then decreased after 1996 because

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of a slowing economy (IEA, 2012; NBS&NEA, 2009) and expansion in the use of clean fuels in the residential sector (IEA, 2012; NBS&NEA, 2009). Anthropogenic emissions gradually decreased in North America, but not in Europe until the late 1980s, because of the difference in promotion of natural gas (IEA, 2012) and emission reductions in transportation. 3.4. Ratios of OC to PM2.5 Based on an previously published primary PM2.5 emission inventory (Huang et al., 2014), fractions of OC in PM2.5 and their variations among sources and regions are derived. The average ratio of OC/PM2.5 for all sources was 41.5%, indicating the important contribution of OC. The ratios were 1.4%, 7.3%, 45.2%, 19.3%, 37.1%, and 58.0% for power plants, industry, residential, transportation, agriculture and wildfires respectively. Variations are due to different combustion efficiencies and abatement facilities. Poor burning conditions in residential and wildfire result in relatively high emissions of OC. Meantime, relatively low ratios in power plants and industry are due to the fact that particle removing facilities are more efficient for coarse particles, while OC is often associated with small ones. High OC to PM2.5 ratios for residential biomass burning were often reported when they were measured simultaneously (Shen et al., 2013a). The contributions of various

sectors to the total emissions of PM2.5 and OC are significantly different. For example, power plants contributed 0.1% and 12.5% of total anthropogenic emissions of OC and PM2.5, respectively (Huang et al., 2014). OC/PM2.5 ratio is high when PM2.5 emissions is dominated by the residential sector or open biomass burning (Fig. S2). High ratios can be found in South America, Africa, the Labrador Peninsula, and southeast Siberia, where wildfires and deforestation dominate total emissions. Extensive use of residential coal and biomass fuels cause high OC/PM2.5 values in Southeast Asia and Central America. In China, the contribution of the power and industrial sectors to PM2.5 reached 54.4% (Huang et al., 2014), producing an OC/PM2.5 ratio of 22.2%, about half the global average. Because OC/PM2.5 ratios are source dependent, any change in source composition can alter the ratios. In Fig. S3, temporal variations of the OC/PM2.5 ratios from 1960 to 2009 are shown for global totals and emissions from individual sectors. Continuously decreasing trends can be seen for emissions from power generation, as a result of both improvements in combustion efficiency and abatement facilities (Zhao et al., 2011; USEIA, 2014). A similar trend can be seen in the transportation sector, reflecting expanding use of diesel fuel (IEA, 2012), which produces emissions with relatively low OC/PM2.5 ratio (average: 0.18) compared with that of gasoline (average: 0.31) (Bond et al., 2004). Although efforts had been made

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Japan, yet 9.28% in China and 53.9% in the Central African Republic (IEA, 2012). In addition, energy efficiencies in the non-residential sectors are substantially different among the three countries. Fig. S4 shows temporal changes of total energy consumption, total OC emissions, and EIs for various anthropogenic sectors from 1960 through 2009. While total anthropogenic energy consumption increased continuously, emissions of OC increased at a much lower pace (or even decreased, as in transportation), because of decreased EFOC values for all anthropogenic sectors. At a global scale, the average EIs decreased by 67% from 1960 to 2009. EIs of the residential sector decreased by 40% over the five decades, even though the total residential fuel consumption increased as a result of population growth. The decrease is partially due to lower EIs for residential coal (7.34%) and residential biomass consumption (12.7%). The former was a consequence of the replacement of chunk coal with honeycomb coal briquettes in China (NBS&NEA, 2009), and the latter by the promotion of improved woodstoves (Shen et al., 2013b). Still, the rapid decrease of residential EIs are not primarily accounted for by the change in EIs of residential coal and biomass fuels. The main driver is replacement of traditional solid fuels with LPG, natural gas, and electricity (IEA, 2012). In Europe, LPG and natural gas accounted for less than 3% of total residential energy consumption in 1960, but 81.4% by 2009 (IEA, 2012). Among the various emission sources, the largest progress in emission control has been with motor vehicles e the effect of higher emission standards and enormous technical progress in engine design and vehicle weight (Timilsina and Dulal, 2009). In the past decades, total oil consumption by motor vehicles increased 484% (IEA, 2012). Yet a decrease in EIs of 94.8% produced a decrease of global OC emissions of 69.5%. The contribution of power plants to total anthropogenic energy consumption almost doubled from 22.9% in 1960 to 41.2% in 2009, but their contribution to total OC emissions declined, since its EI decreased 17.0%. Industry is the only anthropogenic sector where EIs did not decrease continuously over the period. Decreases of industrial EIs before 1990 were due to programs to pulverize boilers and improve emission control equipment (Bond et al., 2007; Zhao et al., 2011). Since 1990 however, rapid expansion of inefficient industries such as coke and brick production drove industrial EIs up (UNIDO, 2008; NBSC, 2011). In Fig. S5, temporal changes of anthropogenic (A) and residential (B) EIs are presented for four regions of East Asia, South and Southeast Asia, Europe, and North Amrica. In all four regions, EIs for the anthropogenic sources decreased substantially. In East Asia and South and Southeast Asia, mean relative EIs dropped from over 100 kg/TJ to less than 50 kg/TJ, primarily because of higher

to improve combustion efficiency and to promote abatement facilities in the industrial sector, the OC/PM2.5 ratio increased from the late 1970s because of the rapid expansion of low-efficiency coke and brick production in developing countries. In the agricultural sector, OC/PM2.5 ratio gradually decreased from 1960 to 1990, because of a proportionate increase of emissions from agricultural machines, which have a relatively low OC/PM2.5 ratio (0.18 on average) (Bond et al., 2004) compared with open burning of agricultural wastes (0.40 on average) (Hays et al., 2005). Because there are no abatement measures in residential fuel combustion, the variations in OC/PM2.5 ratio are based primarily on household fuel types. The slight decreases during two periods e from 1960 to 1974 and from 1996 to 2002 e were due to the replacement of chunk coal with honeycomb coal briquettes (NBS&NEA, 2009), residential coal and biomass fuels with LPG and natural gas (NBS&NEA, 2009; IEA, 2012), and family stoves with centralized heating systems (DHC, 2002). Residential sources are the most important in anthropogenic OC emissions worldwide, so the overall trend of the OC/PM2.5 ratio follow the temporal trend of residential OC emissions. It decreased from 1960 to the mid-1970s, then increased till the late 1990s, and finally decreased again. Efforts have been made for years to promote improved cooking stoves and replace biomass and coal fuels with LPG and natural gas (IEA, 2012; Shen et al., 2013a), which can lead to reduction in the emissions of both OC and PM2.5. The improvements are more efficient for OC than for PM2.5, so campaigns led to a decrease in OC/ PM2.5 ratio in this sector. 3.5. Emission intensity To characterize the relationship between energy consumption and OC emissions, emission densities (EI), defined as quantity of OC emissions per unit of fuel consumption for all sources, were calculated for individual anthropogenic sources (excluding shipping and aviation) for 12 regions (Fig. 6). On global average, EI was 23.0 kg/TJ, while EIs for developed and developing countries were 5.37 kg/TJ and 36.1 kg/TJ, respectively. Very high EI values were found in Africa, especially the Central African Republic (109 kg/TJ) and Democratic Republic of the Congo (190 kg/TJ), because of a combination of extreme underdevelopment (WB, 2012) and heavy dependence on solid fuels in the residential sector (IEA, 2012). The EIs of Asia, Africa, and South America were much higher than those of North America, Oceania, and Europe, owing to differences in energy mix and efficiency. For example, residential solid fuels were almost negligible (0.0029%) in

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Fig. 6. Geographical distribution of country average EIs of all anthropogenic sources excluding shipping and aviation. Fuel consumption profiles with regional average EIs are also shown as pie charts and numbers marked for 12 regions. The regional average EI values are shown in the center of each chart.

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contributions of power stations and industry to the total energy consumption (IEA, 2012). In Europe and North America, the average anthropogenic EIs decreased to less than 10 kg/TJ, mainly because of expanding use of clean fuels (IEA, 2012). Europe and North America reveal their different development stages from rapid developing Asia. In the residential sector, decreases in EIs occurred mainly during the 1960s in North Amrican, during the 1970s and 1980s in Europe, and not until the 1990s in Asia, again indicating different development stages. Based on patterns in the developed world, it appears that there is a significant potential to reduce the OC emissions in the residential sectors of Asian countries, which contribute a large fraction of the total anthropogenic emissions. Although the total quantity of emissions increased during the study period e primarily because of increases in energy consumption driven by economic development and population growth e replacement of dirty fuels with cleaner energy, enforcement of strict regulations, and progress made in abatement technologies resulted in continuous decreases of EIs in almost all sectors of all regions. The decrease of EIs finally offset the increased OC emission activities in 1990s, and further increase is not expected. Rather, if aggressive efforts are made, a gradual decrease in total OC emissions is anticipated. The key to future emission control is residental solid fuel consumption, which contributed more than two thirds of the total anthropogenic OC emissions globally in 2007, with its relative contribution increasing. Replacement of dirty fuels with electricty, LPG, and natural gas in cities of developing countries is an effetive measure for emission reduction. Such replacement occurred in the past and will continue in China and other transitional economies (IEA, 2012; NBS&NEA, 2009). Although it is challenging to replace coal and biomass fuels in rural households because of relatively high costs, some progresses had been made in recent years. For example, the use of honeycomb coal briquettes instead of chunk coal has substantially reduced emissions of OC and other incomplete combustion products in China (Shen et al., 2013a), although the briquettes are not as clean as LPG and electricity (Zhang et al., 2000). Relative contributions of electricity and LPG to rural household energy use for cooking have also recently increased in China (NBS&NEA, 2009). It is widely held that electricity should be the ultimate solution to the household air pollution problem. Still, how to replace coal and biomass fuels for heating in rural households in developing countries remains a challenge. Recognizing the adverse impacts of burning solid fuels in rural households, the Beijing municipal government has developed an ambitious action plan to reduce coal and biomass fuel use in suburban Beijing in coming years (BMCDR, 2007). Meanwhile, rapid urbanization in developing countries will help rural-to-urban migrants to move up the energy ladder and replace biomass fuels (Holdren et al., 2000). Important remaining tasks are to study the costebenefit relationships of various measures and provide more specific recommendations for policy formulation. 4. Conclusions Global total OC emissions were 32.9 Tg in 2007, of which 32.8% was from anthropogenic sources. About 72.2% of anthropogenic emissions were from the residential sector. Globally, the average per-person OC emission were 1.73 kg/year. Per-person OC emission of developing countries was twice as much as that of developed countries, because solid fuels are predominant in residential sector in developing countries, while electricity and other clean fuels are more often used in this sector in developed countries. Emissions were mainly from Asia, Africa and South America. Souce pattens were different among regions, with anthropogenic emissions dominated in Asia and wildfire/deforestation were main soures for the two other regions.

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Global OC emissions increased continuously from 1960 to 1990. Global anthropogenic OC emissions decreased during the period from 1990 to 2000, which were not shown in most other studies. This discrepancy was mainly the result of local information for actitivity rates for China was applied in this study. Though OC emissions have increased for developing countries, both EIs and ratios of OC to PM2.5 have decreased continuously since 1960, reflecting an increasing combustion efficiency. This was drived by the development of both combustion and control tecnologies and the replacement of solid fuels with clean fuels. The highly sectorially and spatially resolved inventory allow detailed cost-benefit analysis on emission control measures which is critical for formulating abatement stragegy. Acknowledgments Funding for this study was provided by the National Natural Science Foundation of China (41390240 and 41130754). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2015.10.017. Conflict of interest The authors declare no competing financial interest. References Beijing Municipal Commission of Development and Reform (BMCDR), 2007. Action Plan for Development of Recycle Economy towards Resource Conservation and Environmental Friendly City (accessed 26.07.14. http://www.bjpc.gov.cn/ywpd/ jnhb12/zcfg/201208/t3747362.htm, 07. Bond, T.C., Streets, D.G., Yarber, K.F., Nelson, S.M., Woo, J.H., Klimont, Z., 2004. A technology-based global inventory of black and organic carbon emissions from combustion. J. Geophys. Res. 109 (D14). Bond, T.C., Bhardwaj, E., Dong, R., Jogani, R., Jung, S.K., Roden, C., Streets, D.G., Trautmann, N.M., 2007. Historical emissions of black and organic carbon aerosol from energy-related combustion, 1850-2000. Glob. Biogeochem. Cycles 21 (2). Cao, G.L., Zhang, X.Y., Zheng, F.C., 2006. Inventory of black carbon and organic carbon emissions from China. Atmos. Environ. 40 (34), 6516e6527. Centre on Emission Inventories and Projections (CEIP): Emissions as used in EMEP models, available at: http://www.ceip.at (accessed 14.08.12). China Health and Nutrition Survey (CHNS); http://www.cpc.unc.edu/projects/china. (accessed 06.11.14). China National Radio(CNR); http://www.cnr.cn/gundong/201201/t20120125_ 509092713.shtml (accessed 11.10.13). Cooke, W.F., Liousse, C., Cachier, H., Feichter, J., 1999. Construction of a 1 degrees x 1 degrees fossil fuel emission data set for carbonaceous aerosol and implementation and radiative impact in the ECHAM4 model. J. Geophys. Res. 104 (D18), 22137e22162. District Heating and Cooling (DHC), 2002. Environmental Technology for the 21st Century. International Energy Agency, Paris. Emission standard for pollutants at idle speed from vehicle with petrol engine. GB3842e83. Emission standard of air pollutants for thermal power plants. GB13223e91. Food and Agriculture Organization of the United Nations (FAO): FAOSTAT Food and Agriculture Statistics; http://faostat.fao.org/default.aspx. (accessed 08.05.2012). Gonçalves, C., Alves, C., Fernandes, A.P., Monteiro, C., Tarelho, L., Evtyugina, M., Pio, C., 2011. Organic compounds in PM 2.5 emitted from fireplace and woodstove combustion of typical Portuguese wood species. Atmos. Environ 45 (27), 4533e4545. Hays, M.D., Fine, P.M., Geron, C.D., Kleeman, M.J., Gullett, B.K., 2005. Open burning of agricultural biomass: physical and chemical properties of particle-phase emissions. Atmos. Environ. 39 (36), 6747e6764. Holdren, J.P., Smith, K.R., Kjellstrom, T., Streets, D., Wang, X., Fischer, S., Green, D., Nagata, E., Slotnick, J., 2000. Energy, the Environment and Health. United Nations Development Programme, New York. Huang, Y., Shen, H.Z., Chen, H., Wang, R., Zhang, Y.Y., Su, S., Chen, Y.C., Lin, N., Zhuo, S.J., Zhong, Q.R., Wang, X.L., Liu, J.F., Li, B.G., Liu, W.X., Tao, S., 2014. Quantification of global primary emissions of PM2.5, PM10, and TSP from combustion and industrial process sources. Environ. Sci. Technol. http://dx.doi.org/ 10.1021/es503696k. International Energy Agency (IEA). IEA World Energy Statistics and Balances; http://

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